Build intelligent fraud detection apps with graph

Fraudsters operate in rings, not in isolation. Build apps that uncover patterns in your data to identify sophisticated scams other tools miss.

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Find fraud fast

Build apps that find fraud patterns 1000x faster than relational databases—no JOINs required.

Continuously evolve fraud apps

Quickly incorporate new data to outpace fraudsters without refactoring the database or rewriting code.

Scale With Any Cloud

Easily integrate an enterprise-grade graph database with your cloud data infrastructure.

Accelerate fraud detection with graph databases

Quickly uncover and investigate complex fraud patterns — as much as 1,000x faster than relational databases.

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Capabilities

Build fraud apps using connected data

Detect hard-to-find fraud patterns

Match recursive and other hard-to-find fraud patterns in data and relationships using the Cypher Query Language. Use powerful graph visualizations to discover new patterns.
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Evolve fraud detection with a flexible knowledge graph

Continuously enrich your knowledge graphs using a flexible graph database schema.
Scale up with any cloud

Easily integrate with native cloud services, scale up to handle increasing user and transaction counts, all with enterprise-grade reliability and security.
Expose fraudsters and fake profiles

Use pathfinding and data science algorithms to find connections to known fraudsters and uncover the hidden intermediaries. Use entity resolution to easily identify fake profiles.

Easily detect money laundering patterns

Evolve your fraud detection apps with a graph database to easily detect money laundering activities. Whether it's a circular movement of money, structured deposits, or inconsistent documentation, you can quickly uncover hidden patterns and suspicious activity.

Learn more about transaction fraud rings

Quickly investigate
claims fraud

Speed up fraud investigations by visualizing the interactions among the insured, third-party providers, experts, and other actors to more easily identify collusion, duplicate claims, and staged losses.

See a claims fraud example

Efficiently uncover
quote fraud

Expose fraudsters, such as ghost brokers, that use fake websites to trick unsuspecting consumers into purchasing insurance or credit instruments through them. Find the hidden connections among seemingly unrelated requests to uncover their fraudulent activities.

Learn about quote fraud

Swiftly identify hidden account takeovers

Identify account takeovers by discovering shared assets being used to access multiple accounts. Trace the flow of fraudulent gains to find out how they exit the fraud network.

Explore account takeover example

Loved by devs. Deployed worldwide.

1,700+ organizations build on Neo4j for data breakthroughs.

Using graph to fight fraud

“If I were to tell our investigators today that we are doing away with Neo4j, there would be a huge outcry. The solution is indispensable for their daily work.”

Paul Kühne
Head of Fraud Prevention
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“For the same false positive rate, we're able to achieve twice the detection rate. And that in the end is less friction for the customer, less losses for the bank, and a better feeling in terms of protection for their customers.”

Edgar Osuna
Chief Data and Analytics Officer
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“Our collaboration with Neo4j helped us develop a successful fraud detection model that met our expectations. It's a win-win fruitful partnership.”

Mehdi Barchouchi
Head of Innovation Data & Tools, Risk Division
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